Literature DB >> 19193775

Two distinct mechanisms shape the reliability of neural responses.

Susanne Schreiber1, Inés Samengo, Andreas V M Herz.   

Abstract

Despite intrinsic noise sources, neurons can generate action potentials with remarkable reliability. This reliability is influenced by the characteristics of sensory or synaptic inputs, such as stimulus frequency. Here we use conductance-based models to study the frequency dependence of reliability in terms of the underlying single-cell properties. We are led to distinguish a mean-driven firing regime, where the stimulus mean is sufficient to elicit continuous firing, and a fluctuation-driven firing regime, where spikes are generated by transient stimulus fluctuations. In the mean-driven regime, the stimulus frequency that induces maximum reliability coincides with the firing rate of the cell, whereas in the fluctuation-driven regime, it is determined by the resonance properties of the subthreshold membrane potential. When the stimulus frequency does not match the optimal frequency, the two firing regimes exhibit different "symptoms" of decreased reliability: reduced spike-time precision and reduced spike probability, respectively. As a signature of stochastic resonance, reliable spike generation in the fluctuation-driven regime can benefit from intermediate amounts of noise that boost spike probability without significantly impairing spike-time precision. Our analysis supports the view that neurons are endowed with selection mechanisms that allow only certain stimulus frequencies to induce reliable spiking. By modulating the intrinsic cell properties, the nervous system can thus tune individual neurons to pick out specific input frequency bands with enhanced spike precision or spike probability.

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Year:  2009        PMID: 19193775     DOI: 10.1152/jn.90711.2008

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  19 in total

1.  GAD67-GFP+ neurons in the Nucleus of Roller. II. Subthreshold and firing resonance properties.

Authors:  J F M van Brederode; A J Berger
Journal:  J Neurophysiol       Date:  2010-11-03       Impact factor: 2.714

2.  Firing-rate resonances in the peripheral auditory system of the cricket, Gryllus bimaculatus.

Authors:  Florian Rau; Jan Clemens; Victor Naumov; R Matthias Hennig; Susanne Schreiber
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2015-08-21       Impact factor: 1.836

3.  Reliability of spike and burst firing in thalamocortical relay cells.

Authors:  Fleur Zeldenrust; Pascal J P Chameau; Wytse J Wadman
Journal:  J Comput Neurosci       Date:  2013-05-25       Impact factor: 1.621

4.  Mode-locked spike trains in responses of ventral cochlear nucleus chopper and onset neurons to periodic stimuli.

Authors:  Jonathan Laudanski; Stephen Coombes; Alan R Palmer; Christian J Sumner
Journal:  J Neurophysiol       Date:  2009-12-30       Impact factor: 2.714

5.  Subthreshold amplitude and phase resonance in models of quadratic type: nonlinear effects generated by the interplay of resonant and amplifying currents.

Authors:  Horacio G Rotstein
Journal:  J Comput Neurosci       Date:  2015-01-15       Impact factor: 1.621

6.  How to correctly quantify neuronal phase-response curves from noisy recordings.

Authors:  Janina Hesse; Susanne Schreiber
Journal:  J Comput Neurosci       Date:  2019-06-24       Impact factor: 1.621

7.  Predicting the response of striatal spiny neurons to sinusoidal input.

Authors:  Charles J Wilson
Journal:  J Neurophysiol       Date:  2017-05-10       Impact factor: 2.714

8.  Fluctuating inhibitory inputs promote reliable spiking at theta frequencies in hippocampal interneurons.

Authors:  Duluxan Sritharan; Frances K Skinner
Journal:  Front Comput Neurosci       Date:  2012-05-24       Impact factor: 2.380

9.  Resonant Interneurons Can Increase Robustness of Gamma Oscillations.

Authors:  Ruben A Tikidji-Hamburyan; Joan José Martínez; John A White; Carmen C Canavier
Journal:  J Neurosci       Date:  2015-11-25       Impact factor: 6.167

10.  Biologically inspired information processing and synchronization in ensembles of non-identical threshold-potential nanostructures.

Authors:  Javier Cervera; José A Manzanares; Salvador Mafé
Journal:  PLoS One       Date:  2013-01-22       Impact factor: 3.240

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